Change detection with adaptive sampling for binary responses

TL;DR

This paper introduces an adaptive sampling method for change detection in systems with multiple lines, improving detection power by allocating more samples to lines likely to have changed, using a Markov decision process framework.

Contribution

It develops a novel adaptive sampling approach formulated as a Markov decision process, optimizing change detection in binary response systems.

Findings

Adaptive sampling favors lines with changes, increasing detection power.

Statistical power improves with larger sample sizes and greater probability differences.

Method outperforms equal randomization in detecting changes for sample sizes of 20 or more.

Abstract

We propose using an adaptive sampling method to detect changes for a system with multiple lines. The adaptive sampling utilizes the information in responses to learn on which line is more likely to have a change thus allocating more units to the line. The learning process is formatted as a Markov decision process by integrating sampling information with likelihood ratio for changes to define rewards and the optimal sampling is approximated by using the Bellman operator iteratively based on the average reward criterion. We demonstrate the performance of the proposed method for binary responses using the exact distribution method for adaptive sampling. Our numeric results show that the adaptive sampling samples more often the line that has a change and the statistical power to detect a change is better than those with the equal randomization for sample sizes of 20 or higher. When sample sizes increase or the difference between out-of-control and in-control probabilities increases, the adaptive sampling allocates higher proportion of units averagely to the line with a change and the statistical power to detect a change increases.